An efficient k-means clustering algorithm: analysis andimplementation
Kanungo, T.
Mount, D.M.
Netanyahu, N.S.
Piatko, C.D.
Silverman, R.
Wu, A.Y.
Almaden Res. Center, San Jose, CA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jul 2002
Volume: 24,
Issue: 7
On page(s): 881-892
ISSN: 0162-8828
References Cited: 50
CODEN: ITPIDJ
INSPEC Accession Number: 7324832
Digital Object Identifier: 10.1109/TPAMI.2002.1017616
Current Version Published: 2002-08-07
Abstract
In k-means clustering, we are given a set of n data points in
d-dimensional space Rd and an integer k and the problem is to
determine a set of k points in Rd, called centers, so as to minimize the
mean squared distance from each data point to its nearest center. A
popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We
present a simple and efficient implementation of Lloyd's k-means
clustering algorithm, which we call the filtering algorithm. This
algorithm is easy to implement, requiring a kd-tree as the only major
data structure. We establish the practical efficiency of the filtering
algorithm in two ways. First, we present a data-sensitive analysis of
the algorithm's running time, which shows that the algorithm runs faster
as the separation between clusters increases. Second, we present a
number of empirical studies both on synthetically generated data and on
real data sets from applications in color quantization, data
compression, and image segmentation
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